Top Data & Analytics Jobs

Leads Expedia Group’s data engineering, software engineering, AI, and innovation capabilities for Marketing Automation and MarTech. Sets enterprise technology strategy, oversees scalable data platforms, pipelines, APIs, integrations, governance, security, observability, and responsible AI foundations. Builds high-performing engineering teams, manages modernization and platform investments, enables analytics and experimentation, and partners with senior stakeholders to improve customer engagement, operational efficiency, and technology delivery.

Leads data engineering, data science, and AI initiatives, including Snowflake architecture, ETL pipelines, AWS data systems, data warehousing, analytics, and AI/ML solutions. Manages data governance, dashboards, experimentation, and web analytics integrations while building and mentoring multidisciplinary teams. Partners with Product, Editorial, Engineering, and business stakeholders to translate data needs into scalable solutions and drive business impact.

Develop and monitor fraud-detection models and pattern-recognition algorithms for lending products. Analyze large datasets using Python and SQL, identify emerging fraud trends, and collaborate with Fraud Operations to refine features and reduce false positives and negatives. Conduct ad hoc analyses, recommend risk and verification strategies, communicate findings to cross-functional partners, and support improvements to underwriting and verification processes.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Leads quantitative model risk analysis for loan loss forecasting and allowance for credit losses. Evaluates model soundness, performance under stress scenarios, regulatory alignment, and model risk. Develops alternative approaches, applies econometric, statistical, and machine learning methods, improves model frameworks, documents findings, and communicates complex results to management, regulators, and technical audiences. Collaborates with model development and risk teams while supporting continuous improvement and Agile delivery.

Validates and enhances balance sheet forecasting, stress testing, capital markets, and financial risk models. Develops quantitative and machine learning solutions, analyzes large datasets, performs model testing and benchmarking, documents findings, and communicates results to business leaders and technical stakeholders. Collaborates across teams to build cloud-based analytical solutions and improve risk management processes.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Provides business intelligence and automation for Regulatory Operations through data analysis, process simplification, stakeholder collaboration, and compelling data storytelling. Builds automation solutions using Alteryx, SQL, Python, Snowflake, AWS, APIs, machine learning, and LLMs. Identifies operational improvement opportunities, delivers actionable insights, and partners with subject matter experts and management to implement efficiency-enhancing solutions.

Owns Small Business Deposits data across sourcing, documentation, delivery, quality, protection, and governance. Partners with product, technology, analytics, operations, risk, controls, and vendors to define data assets, requirements, access controls, retention, and risk mitigation. Leads modernization into enterprise data platforms, resolves delivery issues, aligns stakeholders, and manages direct or matrixed teams delivering data-related outcomes.

Leads the Federal Reserve Bank of Boston’s Data & Analytics department, setting strategy, architecture, governance, and multi-year technical roadmaps. Oversees self-service analytics, data infrastructure, pipelines, curated datasets, visualizations, and decision-support tools. Builds stakeholder partnerships, ensures responsible AI use, manages operational risks and controls, and develops a high-performing multidisciplinary team. Represents the function to executive leadership and collaborates across the Federal Reserve System.

Leads enterprise internal fraud analytics, monitoring, and response initiatives. Uses SAS and SQL to analyze complex data, develop fraud rules, identify anomalous activity, and design proactive monitoring solutions. Partners with executives, risk teams, audit, and control functions to deliver defensible, well-governed analytics that reduce fraud risk and withstand regulatory scrutiny. Provides subject matter expertise, consultative guidance, and executive-level communication in an ambiguous, high-scrutiny environment.

Leads enterprise internal fraud analytics and monitoring initiatives using SAS, SQL, and advanced analytical methods. Analyzes complex data, develops fraud rules and detection strategies, advises executives and risk stakeholders, and delivers defensible solutions meeting regulatory and audit standards. Partners across risk, control, audit, and business teams to reduce fraud exposure, govern monitoring approaches, and translate complex requirements into actionable analytical solutions.

Own the analytics lifecycle from complex raw data through executive-ready insights. Design experiments, analyze performance drivers, build scalable data pipelines and analytical frameworks, and communicate findings to marketing, product, and technology stakeholders. The role supports customer acquisition, owned media optimization, repeatable reporting, and roadmap decisions through secure, maintainable SQL and Python or R solutions.

Develop and deploy advanced statistical and ML models on large-scale alternative datasets to generate investable signals, partner with investment teams, evaluate data quality, and drive novel data-driven research with commercial impact.

As a Quantitative Research Engineer, design, develop, and implement software for automated trading systems using advanced statistical techniques. Collaborate with researchers and prioritize business needs.

Evaluate Business Banking deposit and cash management products using data-driven analysis. Develop pricing strategies, analyze small-business banking behavior, create dashboards and reports, build data assets, automate processes, and provide insights for product development. Collaborate with Product, Design, and Technology teams, participate in senior product reviews, and present recommendations to business leaders.

Develop causal economic analyses using Mastercard and public datasets. Translate business questions into identification strategies, build econometric and causal machine-learning models, lead projects from data ingestion through validation, and communicate findings to clients and stakeholders. Guide best practices, prototype scalable analytics, conduct research, and apply experimentation methods across business contexts.

Develop causal economic analyses using Mastercard and public datasets to assess business decisions, market changes, events, and external shocks. Translate business questions into empirical designs, lead projects from data ingestion through model validation, prototype scalable causal inference methods, guide best practices, and communicate findings to internal and external stakeholders. The role requires econometric and causal machine-learning expertise, experimentation experience, and strong technical communication skills.

Designs and maintains data pipelines supporting monthly profitability close, automates manual finance workflows, and develops machine learning solutions for quality control, anomaly detection, and reconciliations. The role extracts and delivers financial data, prototypes LLM and agentic tools, and partners with Finance, Technology, and P&A on analytics initiatives. It also communicates technical methods and results to senior business stakeholders.

Leads global network economics and value optimization initiatives through data-driven pricing, interchange, revenue, and profitability analysis. Develops predictive models, simulations, segmentation frameworks, reporting, visualizations, data pipelines, and scalable analytics tools using large datasets. Partners with global and regional pricing teams, translates stakeholder needs into analytical solutions, presents actionable findings, manages project delivery, and mentors junior colleagues.

Analyze business data, uncover operational improvement opportunities, and develop automation and self-service solutions. The role partners with stakeholders and subject matter experts, performs ad hoc analysis, communicates actionable insights, and leads simplification projects. It requires experience with analytics and automation tools including Alteryx, Tableau, SAS, SQL, Python, Snowflake, AWS, APIs, and AI/ML concepts. This is an onsite role five days per week and does not offer visa sponsorship.

Leads a global organization spanning data science, data architecture and engineering, and reporting and insights. Develops enterprise data strategy, oversees data products and analytics initiatives, advances predictive and prescriptive insights, establishes governance and quality standards, and partners with executives to deliver measurable business value. Builds high-performing teams, develops leaders, prioritizes investments, and drives scalable, trusted, decision-oriented analytics capabilities.

Designs and optimizes Snowflake data pipelines for clinical and claims data supporting HEDIS processing, quality analytics, chart abstraction, and care gap identification. Integrates AI, machine learning, and NLP models into healthcare data workflows; implements data quality, governance, security, and HIPAA-compliant practices. Partners with clinical quality and reporting teams to translate HEDIS specifications into automated solutions, while mentoring engineers and advancing MLOps and DataOps practices.

Build predictive models and customer economics analyses that forecast behavior, optimize marketing investments, and support revenue growth. Develop automated tools, dashboards, and repeatable frameworks to communicate customer performance and retention trends. Partner with Marketing, Finance, Data Science, and Data Engineering to translate complex model outputs into actionable business strategies using technologies including Databricks, Airflow, Bitbucket, SQL, and Snowflake.

Build predictive models forecasting customer behavior, analyze retention and gameplay trends, and create automated tools, analyses, and dashboards. Translate model outputs into actionable recommendations that guide marketing investments, promotional strategies, customer valuation, and retention. Partner with Marketing, Finance, Data Science, and Data Engineering teams while using Databricks, Airflow, Bitbucket, SQL, Snowflake, and dashboarding tools to support revenue growth and customer insights.